Beitrag zur Theorie des Ferromagnetismus

  title={Beitrag zur Theorie des Ferromagnetismus},
  author={Ernst Ising},
  journal={Zeitschrift f{\"u}r Physik},
  • E. Ising
  • Published 1 February 1925
  • Physics
  • Zeitschrift für Physik

On testing for parameters in Ising models

  • R. MukherjeeG. Ray
  • Computer Science
    Annales de l'Institut Henri Poincaré, Probabilités et Statistiques
  • 2022
A general lower bound of minimax separation rates is provided which yields sharp results in high temperature regimes and lower bounds for estimation and testing rates are derived in two parameter Ising models -- which turn out to be optimal according to several recent results in this area.

On the parameter learning for Perturb-and-MAP models

It appears that one can apply a stochastic technique over the proposed perturb-and-map approximation approach and still maintain convergence while make it faster in practice, which is an efficient and scalable generalization of the parameter learning approach.

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

This work explores minimum energy flow (MEF) as a scalable convex objective for determining network parameters and catalogs various properties of MEF, such as biological plausibility, and then compares to classical approaches in the theory of learning.

On Model Selection Consistency of Lasso for High-Dimensional Ising Models on Tree-like Graphs

It is rigorously proved that under some mild coherence conditions on the population covariance matrix of the Ising model, consistent model selection can be achieved with sample sizes n = Ω(d log p) for any tree-like graph in the paramagnetic phase.

An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified states*

The theoretical results of the model selection method are extended to show that the method will still correctly identify edges in the underlying graphical model under suitable mis classify settings, and an expectation maximization algorithm is developed that accounts for misclassification during model selection.

Statistical Matching of Categorial Data with Markov Networks

The theory of statistical matching, and the theory of probabilistic graphical models with a focus on Markov networks are summarized, and an equation with which the joint probability distribution of two disjoint datasets can be estimated is offered.

Global testing against sparse alternatives under Ising models

A testing procedure is developed that is broadly applicable to account for dependence and it is shown that it is asymptotically minimax optimal under fairly general regularity conditions.

Simulações entrópicas do Modelo de Baxter-Wu

Neste trabalho, utilizamos uma tecnica de amostragem entropica com refinamentos baseada no metodo de Wang-Landau e tecnicas de escala de tamanho finito para estudar variacoes do modelo de

A Simulated Annealing Based Method for Generating Random Metabolic Networks

A new method for randomization of metabolic networks was developed based on a Simulated Annealing algorithm, which was able to generate random networks with a lower number of blocked reactions while the total number of reactions remained stable.

Analytic Combinatorics in Several Variables : Effective Asymptotics and Lattice Path Enumeration. (Combinatoire analytique en plusieurs variables : asymptotique efficace et énumération de chemin de treillis)

This thesis gives several new applications of ACSV to the enumeration of lattice walks restricted to certain regions, developing rigorous algorithms and giving the firstcomplexity results in this area under conditions which are broadly satisfied.